Our approach achieves 0.942 mAP using an object detection model, that outperforms current state of the art accuracy defined by Ferguson et al. [1]} without using segmentation techniques. Our Automated Defect Recognition (ADR) is based on a CNN RetinaNet model [2]} combined with a Feature Pyramid Network (FPN) [3]} and an upscaling of the input images in order to improve its detection performance at different defect scales.
